• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.13-17
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• 2024

In this paper, union-based rule antecedent neuro-fuzzy controller, which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The proposed neuro-fuzzy controller allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the proposed neuro-fuzzy controller, we consider the multiple-term unified logic processor (MULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, the genetic algorithm (GA) constructs a Boolean skeleton of the proposed neuro-fuzzy controller, while the stochastic reinforcement learning refines the binary connections of the GA-optimized controller for further improvement of the performance index. An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation and experiment.

• Journal of KSNVE
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• v.6 no.6
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• pp.827-834
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• 1996

The effect of pipe breaks in the secondary side is investigated as a part of the fuel assembly qualification program. Using the detailed dynamic analysis of a reactor core, peak responses for the motions induced from pipe breaks are obtained for a detailed core model. The secondary side pipe breaks such as main steam line and economizer feedwater line braksare considered because leak-before-break methodology has provided a technical basis for the elimination of double ended guillotine breaks of all high energy piping systems with a diameter of 10 inches or over in the primary side from the design basis. The dynamic responses such as fuel assembly shear force, bending moment, axial force and displacement, and spacer grid impact loads are carefully investigated. Also, the stress analysis is performed and the effect of the secondary side pipe breaks on the fuel assembly structural integrity under the faulted condition is addressed.

• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1057-1068
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• 2005

This paper proposes a real-time face detection system which detects multiple faces from low resolution video such as web-camera video. First, It finds face region candidates by using AdaBoost based object detection method which selects a small number of critical features from a larger set. Next, it generates reduced feature vector for each face region candidate by using principle component analysis. Finally, it classifies if the candidate is a face or non-face by using SVM(Support Vector Machine) based binary classification. According to experiment results, the proposed method achieves real-time face detection from low resolution video. Also, it reduces the false detection rate than existing methods by using PCA and SVM based face classification step.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3A
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• pp.324-330
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• 2004

In this paper, we propose the TCM(Trellis coded modulation) decoding scheme that reduces the number of operations in branch metric with STBC(space time block codes) channel information and present the implementation results. The proposed TCM decoding scheme needs only 1 signal point in each TCM subset. Using bias point scheme, It detects the minimum distance symbol. The proposed TCM decoding scheme can reduce the branch metric calculations. In case of 16QAM 8 subset, the reduction ratio is about 50％ and for 64QAM 8 subset, about 80％ reduction can be obtained. The results of logic synthesis for the TCM and STBC decoder with the proposed scheme are 57.6K gate count.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.313-316
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• 2011

The skyline of a multi-dimensional data set is a subset that consists of the data that are not dominated by other members of the set. Skyline computation can be very useful for decision making for multi-dimensional data set. However, in case that the skyline is very large, it may not be much useful for decision making. In this paper, we propose an algorithm for computing a part of the skyline considering location restrictions that the user provides, such as origin movement, degree ranges and/or distances from the origin. The algorithm eliminates noncandidate data rapidly, and returns in order the skyline points that satisfy the user's requests. We show that the algorithm is efficient by experiments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1065-1078
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• 2014

Considering the safety of the data and performance, it can be said that the performance of the AES algorithm in a symmetric key-based encryption is the best in the IPSec-based VPN. When using the AES algorithm in IPSec-based VPN even with the expensive hardware encryption card such as OCTEON Card series of Cavium Networks, the Performance of VPN works less than half of the firewall using the same hardware. In 2008, Intel announced a set of 7 AES-NI instructions in order to improve the performance of the AES algorithm on the Intel CPU. In this paper, we verify how much the performance IPSec-based VPN can be improved when using seven sets of AES-NI instruction of the Intel CPU.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.252-261
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• 2012

This papers proposes a motion vector coding method which selects the optimal predictive motion vector after excluding some candidate predictive motion vectors in the minimum rate-distortion sense, and prunes the candidate predictive motion vectors by a contradiction testing that operates under a predefined criterion at both encoder and decoder for reducing the signaling information. Experimental results show that the proposed method gains in BDBR by up to 1.8% compared to the H.264/AVC.

• Journal of Korea Multimedia Society
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• v.3 no.6
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• pp.592-603
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• 2000

This paper is concerning off-line signature verification using a density function which is obtained by convolving the signature image with twelve-directional $5\times{5}$ gradient masks and the weighted fuzzy mean classifier. The twelve-directional density function based on Nevatia-Babu template gradient is related to the overall shape of a signature image and thus, utilized as a feature set. The weighted fuzzy mean classifier with the reference feature vectors extracted from only genuine signature samples is evaluated for the verification of freehand forgeries. The experimental results show that the proposed system can classify a signature whether it is genuine or forged with more than 98% overall accuracy even without any knowledge of varied freehand forgeries.

• Journal of Energy Engineering
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• v.24 no.1
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• pp.149-163
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• 2015

In this study, we conducted study on the confirmation of thermal-hydraulic safety for Mast assembly with Computational Fluid Dynamics(CFD) analysis. Before performing the natural convection analysis for the Mast assembly by using CFD code, we validated the CFD code against two benchmark natural convection data for the evaluation of turbulence models and confirmation of its applicability to the natural convection flow. From the first benchmark test which was performed by Betts et al. in the simple rectangular channel, we selected standard k-omega turbulence model for natural convection. And then, calculation performance of CFD code was also investigated in the sub-channel of rod bundle by comparing with PNL(Pacific Northwest Laboratory) experimental data and prediction results by MATRA and Fluent 12.0 which were performed by Kwon et al.. Finally, we performed main natural convection analysis for fuel assembly inside the Mast assembly by using validated turbulence model. From the calculation, we observed stable natural circulation flow between the mast assembly and pool side and evaluated the thermal-hydraulic safety by calculating the departure from nucleate boiling ratio.